Following previous works [1]}, [2]} on visual explanation, we conduct weakly supervised localization experiment in the context of metric learning. Among the popular datasets for image retrieval, CUB [3]} is a challenging dataset with over 10,000 images from 200 bird species, and the bounding box annotation is available for localization evaluation. We first train a metric learning model on CUB using the state-of-the-art image retrieval method [4]}. Then the weakly supervised localization is conducted to show the differences between the variants of the proposed visual explanation method. We first generate the activation maps of different methods, then the mask is generated by segmenting the heatmap with a threshold. Following previous works [1]}, [2]}, the bounding box that covers the largest connected component in the mask is generated as the prediction. A predicted bounding box is considered correct if the IoU (Intersection over Union) between the bounding box and ground-truth is greater than \(0.5\) .
We evaluate multiple thresholds for different approaches and find the results are sensitive to the threshold. Table REF  lists the localization accuracy on CUB validation set under different thresholds.
For convenience, we report the best result of each method in Table REF , including the result of CAM (classification based method) adopted from the original paper [1]}.
